MétaCan
Menu
Back to cohort
Record W3115221245 · doi:10.1016/j.jiph.2020.12.019

COVID-19 with spontaneous pneumothorax, pneumomediastinum, and subcutaneous emphysema in the intensive care unit: Two case reports

2020· article· en· W3115221245 on OpenAlexaff
Abdulrahman Alharthy, Gultakin Bakirova, Homaida Bakheet, Abdullah Balhamar, Peter G. Brindley, Saleh A. Alqahtani, Ziad A. Memish, Dimitrios Karakitsos

Bibliographic record

VenueJournal of Infection and Public Health · 2020
Typearticle
Languageen
FieldMedicine
TopicPneumothorax, Barotrauma, Emphysema
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineSubcutaneous emphysemaPneumothoraxPneumomediastinumIntensive care unitCoronavirus disease 2019 (COVID-19)Pericardial effusionLungPneumoniaRadiologyIntensive care medicineSurgeryPathologyInternal medicineDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Real-Time-reverse-transcription-Polymerase-Chain-Reaction from nasopharyngeal swabs and chest computed tomography (CT) depicting typically bilateral ground-glass opacities with a peripheral and/or posterior distribution are mandatory in the diagnosis of COVID-19. COVID-19 pneumonia may present though with atypical features such as pleural and pericardial effusions, lymphadenopathy, cavitations, and CT halo sign. In these two case-reports, COVID-19 presented as pneumothorax, pneumomediastinum and subcutaneous emphysema in critically ill patients. These disorders may require treatment or can be even self-limiting. Clinicians should be aware of their potential effects on the cardiorespiratory status of critically ill COVID-19 patients. Finally, pneumothorax can be promptly diagnosed by means of lung ultrasound. Although operator dependent, lung ultrasound is a useful bedside diagnostic tool that could alleviate the risk of cross-infection related to COVID-19 patient transport.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0090.006
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.068
GPT teacher head0.355
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Infection and Public HealthSame topicPneumothorax, Barotrauma, EmphysemaFrench-language works237,207